v0.99.7 -- run a test for r-lib/pkgdown#1230. Works locally

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: spatialLIBD
 Title: LIBD Visium spatial transcriptomics human pilot data inspector
-Version: 0.99.6
+Version: 0.99.7
 Date: 2020-02-27
 Authors@R:
     c(

NEWS.md

@@ -1,3 +1,7 @@
+# spatialLIBD 0.99.7
+
+* Run a test that might help with https://github.com/r-lib/pkgdown/issues/1230.
+
 # spatialLIBD 0.99.6
 
 * Add mirrors for the shiny app and change the main location.

R/check_image_path.R

@@ -13,13 +13,13 @@
 #' @examples
 #'
 #' ## Obtain the necessary data
-#' if (!exists('ori_sce')) ori_sce <- fetch_data('sce')
+#' if (!exists('sce')) sce <- fetch_data('sce')
 #'
 #' ## Get the path to the images
 #' img_path <- system.file('app', 'www', 'data', package = 'spatialLIBD')
 #'
 #' ## Check the object
-#' check_image_path(img_path, ori_sce)
+#' check_image_path(img_path, sce)
 #'
 
 check_image_path <- function(image_path, sce) {

R/check_modeling_results.R

@@ -12,11 +12,11 @@
 #'
 #' @examples
 #'
-#' if (!exists('ori_modeling_results'))
-#'     ori_modeling_results <- fetch_data(type = 'modeling_results')
+#' if (!exists('modeling_results'))
+#'     modeling_results <- fetch_data(type = 'modeling_results')
 #'
 #' ## Check the object
-#' xx <- check_modeling_results(ori_modeling_results)
+#' xx <- check_modeling_results(modeling_results)
 #'
 
 check_modeling_results <- function(modeling_results) {

R/check_sce.R

@@ -16,10 +16,10 @@
 #' @examples
 #'
 #' ## Obtain the necessary data
-#' if (!exists('ori_sce')) ori_sce <- fetch_data('sce')
+#' if (!exists('sce')) sce <- fetch_data('sce')
 #'
 #' ## Check the object
-#' check_sce(ori_sce)
+#' check_sce(sce)
 #'
 
 check_sce <- function(sce,

R/check_sce_layer.R

@@ -15,10 +15,10 @@
 #' @examples
 #'
 #' ## Obtain the necessary data
-#' if (!exists('ori_sce_layer')) ori_sce_layer <- fetch_data('sce_layer')
+#' if (!exists('sce_layer')) sce_layer <- fetch_data('sce_layer')
 #'
 #' ## Check the object
-#' check_sce_layer(ori_sce_layer)
+#' check_sce_layer(sce_layer)
 #'
 
 check_sce_layer <- function(sce_layer, variables = 'layer_guess_reordered_short') {

R/fetch_data.R

@@ -42,7 +42,7 @@
 #'
 #' ## Download the SingleCellExperiment object
 #' ## at the layer-level
-#' sce_layer <- fetch_data('sce_layer')
+#' if (!exists('sce_layer')) sce_layer <- fetch_data('sce_layer')
 #'
 #' ## Explore the data
 #' sce_layer

R/gene_set_enrichment.R

@@ -41,13 +41,13 @@
 #' )
 #'
 #' ## Obtain the necessary data
-#' if (!exists('ori_modeling_results'))
-#'     ori_modeling_results <- fetch_data(type = 'modeling_results')
+#' if (!exists('modeling_results'))
+#'     modeling_results <- fetch_data(type = 'modeling_results')
 #'
 #' ## Compute the gene set enrichment results
 #' asd_sfari_enrichment <- gene_set_enrichment(
 #'     gene_list = asd_sfari_geneList,
-#'     modeling_results = ori_modeling_results,
+#'     modeling_results = modeling_results,
 #'     model_type = 'enrichment'
 #' )
 #'

R/gene_set_enrichment_plot.R

@@ -52,13 +52,13 @@
 #' )
 #'
 #' ## Obtain the necessary data
-#' if (!exists('ori_modeling_results'))
-#'     ori_modeling_results <- fetch_data(type = 'modeling_results')
+#' if (!exists('modeling_results'))
+#'     modeling_results <- fetch_data(type = 'modeling_results')
 #'
 #' ## Compute the gene set enrichment results
 #' asd_sfari_enrichment <- gene_set_enrichment(
 #'     gene_list = asd_sfari_geneList,
-#'     modeling_results = ori_modeling_results,
+#'     modeling_results = modeling_results,
 #'     model_type = 'enrichment'
 #' )
 #'

R/geom_spatial.R

@@ -21,11 +21,11 @@
 #'
 #' @examples
 #' ## Obtain the necessary data
-#' if (!exists('ori_sce')) ori_sce <- fetch_data('sce')
+#' if (!exists('sce')) sce <- fetch_data('sce')
 #'
 #' ## Select the first sample and extract the data
-#' sample_id <- unique(ori_sce$sample_name)[1]
-#' sce_sub <- ori_sce[, ori_sce$sample_name == sample_id]
+#' sample_id <- unique(sce$sample_name)[1]
+#' sce_sub <- sce[, sce$sample_name == sample_id]
 #' sample_df <- as.data.frame(colData(sce_sub))
 #'
 #' ## Make a plot using geom_spatial
@@ -41,6 +41,9 @@
 #'         y = 0.5
 #'     )
 #'
+#' ## Clean up
+#' rm(sce_sub)
+#'
 
 geom_spatial <-  function(mapping = NULL,
     data = NULL,

R/get_colors.R

@@ -19,15 +19,15 @@
 #' @examples
 #'
 #' ## Obtain the necessary data
-#' if (!exists('ori_sce_layer')) ori_sce_layer <- fetch_data('sce')
+#' if (!exists('sce_layer')) sce_layer <- fetch_data('sce')
 #'
 #' ## Example layer colors with the corresponding names
-#' get_colors(libd_layer_colors, ori_sce_layer$layer_guess)
-#' get_colors(libd_layer_colors, ori_sce_layer$layer_guess_reordered_short)
+#' get_colors(libd_layer_colors, sce_layer$layer_guess)
+#' get_colors(libd_layer_colors, sce_layer$layer_guess_reordered_short)
 #'
 #' ## Example where colors are assigned automatically
 #' ## based on a pre-defined set of colors
-#' get_colors(clusters = ori_sce_layer$kmeans_k7)
+#' get_colors(clusters = sce_layer$kmeans_k7)
 #'
 #' ## Example where Polychrome::palette36.colors() gets used
 #' get_colors(clusters = letters[seq_len(13)])

R/layer_boxplot.R

@@ -36,39 +36,39 @@
 #' @examples
 #'
 #' ## Obtain the necessary data
-#' if (!exists('ori_modeling_results'))
-#'     ori_modeling_results <- fetch_data(type = 'modeling_results')
-#' if (!exists('ori_sce_layer')) ori_sce_layer <- fetch_data(type = 'sce_layer')
+#' if (!exists('modeling_results'))
+#'     modeling_results <- fetch_data(type = 'modeling_results')
+#' if (!exists('sce_layer')) sce_layer <- fetch_data(type = 'sce_layer')
 #'
 #' ## Top 2 genes from the enrichment model
 #' sig_genes <- sig_genes_extract_all(n = 2,
-#'     modeling_results = ori_modeling_results,
-#'     sce_layer = ori_sce_layer)
+#'     modeling_results = modeling_results,
+#'     sce_layer = sce_layer)
 #'
 #' ## Example default boxplot
-#' layer_boxplot(sig_genes = sig_genes, sce_layer = ori_sce_layer)
+#' layer_boxplot(sig_genes = sig_genes, sce_layer = sce_layer)
 #'
 #' ## Now show the long title version
 #' layer_boxplot(sig_genes = sig_genes,
 #'     short_title = FALSE,
-#'     sce_layer = ori_sce_layer)
+#'     sce_layer = sce_layer)
 #'
 #' layer_boxplot(
 #'     i = which(sig_genes$model_type == 'anova')[1],
 #'     sig_genes = sig_genes,
-#'     sce_layer = ori_sce_layer
+#'     sce_layer = sce_layer
 #' )
 #' layer_boxplot(
 #'     i = which(sig_genes$model_type == 'pairwise')[1],
 #'     sig_genes = sig_genes,
-#'     sce_layer = ori_sce_layer
+#'     sce_layer = sce_layer
 #' )
 #'
 #' ## Viridis colors displayed in the shiny app
 #' library('viridisLite')
 #' layer_boxplot(
 #'     sig_genes = sig_genes,
-#'     sce_layer = ori_sce_layer,
+#'     sce_layer = sce_layer,
 #'     col_low_box = viridis(4)[2],
 #'     col_low_point = viridis(4)[1],
 #'     col_high_box = viridis(4)[3],
@@ -78,7 +78,7 @@
 #' ## Paper colors displayed in the shiny app
 #' layer_boxplot(
 #'     sig_genes = sig_genes,
-#'     sce_layer = ori_sce_layer,
+#'     sce_layer = sce_layer,
 #'     col_low_box = 'palegreen3',
 #'     col_low_point = 'springgreen2',
 #'     col_high_box = 'darkorange2',
@@ -89,7 +89,7 @@
 #' layer_boxplot(
 #'     i = which(sig_genes$model_type == 'pairwise')[1],
 #'     sig_genes = sig_genes,
-#'     sce_layer = ori_sce_layer,
+#'     sce_layer = sce_layer,
 #'     col_bkg_box = 'grey90',
 #'     col_bkg_point = 'grey60',
 #'     col_low_box = 'lightcyan',

R/layer_stat_cor.R

@@ -24,13 +24,13 @@
 #' @examples
 #'
 #' ## Obtain the necessary data
-#' if (!exists('ori_modeling_results'))
-#'     ori_modeling_results <- fetch_data(type = 'modeling_results')
+#' if (!exists('modeling_results'))
+#'     modeling_results <- fetch_data(type = 'modeling_results')
 #'
 #' ## Compute the correlations
 #' cor_stats_layer <- layer_stat_cor(
 #'     tstats_Human_DLPFC_snRNAseq_Nguyen_topLayer,
-#'     ori_modeling_results,
+#'     modeling_results,
 #'     'enrichment'
 #' )
 #'

R/layer_stat_cor_plot.R

@@ -31,13 +31,13 @@
 #' @examples
 #'
 #' ## Obtain the necessary data
-#' if (!exists('ori_modeling_results'))
-#'     ori_modeling_results <- fetch_data(type = 'modeling_results')
+#' if (!exists('modeling_results'))
+#'     modeling_results <- fetch_data(type = 'modeling_results')
 #'
 #' ## Compute the correlations
 #' cor_stats_layer <- layer_stat_cor(
 #'     tstats_Human_DLPFC_snRNAseq_Nguyen_topLayer,
-#'     ori_modeling_results,
+#'     modeling_results,
 #'     'enrichment'
 #' )
 #'

R/run_app.R

@@ -93,7 +93,7 @@ run_app <- function(sce = fetch_data(type = 'sce'),
     sce <-
         check_sce(sce,
             variables = c(spatial_libd_var, sce_discrete_vars, sce_continuous_vars))
-    sce_layer <-
+    if (!exists('sce_layer')) sce_layer <-
         check_sce_layer(sce_layer, variables = spatial_libd_var)
     modeling_results <- check_modeling_results(modeling_results)
     image_path <- check_image_path(image_path, sce)

R/sce_image_clus.R

@@ -27,14 +27,14 @@
 #' @examples
 #'
 #' ## Obtain the necessary data
-#' if (!exists('ori_sce')) ori_sce <- fetch_data('sce')
+#' if (!exists('sce')) sce <- fetch_data('sce')
 #'
 #' ## Check the colors defined by Lukas M Weber
 #' libd_layer_colors
 #'
 #' ## Use the manual color palette by Lukas M Weber
 #' sce_image_clus(
-#'     sce = ori_sce,
+#'     sce = sce,
 #'     clustervar = 'layer_guess_reordered',
 #'     sampleid = '151673',
 #'     colors = libd_layer_colors,
@@ -43,7 +43,7 @@
 #'
 #' ## Without histology
 #' sce_image_clus(
-#'     sce = ori_sce,
+#'     sce = sce,
 #'     clustervar = 'layer_guess_reordered',
 #'     sampleid = '151673',
 #'     colors = libd_layer_colors,

R/sce_image_clus_p.R

@@ -18,8 +18,8 @@
 #' @examples
 #'
 #' ## Obtain the necessary data
-#' if (!exists('ori_sce')) ori_sce <- fetch_data('sce')
-#' sce_sub <- ori_sce[, ori_sce$sample_name == '151673']
+#' if (!exists('sce')) sce <- fetch_data('sce')
+#' sce_sub <- sce[, sce$sample_name == '151673']
 #'
 #' ## Use the manual color palette by Lukas M Weber
 #' ## Don't plot the histology information
@@ -33,6 +33,9 @@
 #'     spatial = FALSE
 #' )
 #'
+#' ## Clean up
+#' rm(sce_sub)
+#'
 
 sce_image_clus_p <-
     function(sce,

R/sce_image_gene.R

@@ -31,23 +31,23 @@
 #' @examples
 #'
 #' ## Obtain the necessary data
-#' if (!exists('ori_sce')) ori_sce <- fetch_data('sce')
+#' if (!exists('sce')) sce <- fetch_data('sce')
 #'
 #' ## Valid `geneid` values are those in
-#' head(rowData(ori_sce)$gene_search)
-#' ## or continuous variables stored in colData(ori_sce)
+#' head(rowData(sce)$gene_search)
+#' ## or continuous variables stored in colData(sce)
 #'
 #' ## Visualize a default gene on the non-viridis scale
 #' sce_image_gene(
-#'     sce = ori_sce,
+#'     sce = sce,
 #'     sampleid = '151507',
 #'     viridis = FALSE
 #' )
 #'
 #' ## Visualize a continuous variable, in this case, the ratio of chrM
 #' ## gene expression compared to the total expression at the spot-level
 #' sce_image_gene(
-#'     sce = ori_sce,
+#'     sce = sce,
 #'     sampleid = '151507',
 #'     geneid = 'expr_chrM_ratio'
 #' )

R/sce_image_gene_p.R

@@ -20,10 +20,10 @@
 #' @examples
 #'
 #' ## Obtain the necessary data
-#' if (!exists('ori_sce')) ori_sce <- fetch_data('sce')
+#' if (!exists('sce')) sce <- fetch_data('sce')
 #'
 #' ## Prepare the data for the plotting function
-#' sce_sub <- ori_sce[, ori_sce$sample_name == '151673']
+#' sce_sub <- sce[, sce$sample_name == '151673']
 #' df <- as.data.frame(colData(sce_sub))
 #' df$COUNT <- df$expr_chrM_ratio
 #'
@@ -37,6 +37,9 @@
 #'     spatial = FALSE
 #' )
 #'
+#' ## Clean up
+#' rm(sce_sub)
+#'
 
 sce_image_gene_p <-
     function(sce,

R/sce_image_grid.R

@@ -24,10 +24,10 @@
 #' @examples
 #'
 #' ## Obtain the necessary data
-#' if (!exists('ori_sce')) ori_sce <- fetch_data('sce')
+#' if (!exists('sce')) sce <- fetch_data('sce')
 #'
 #' ## Subset to two samples of interest
-#' sce_sub <- ori_sce[, ori_sce$sample_name %in% c('151673', '151674')]
+#' sce_sub <- sce[, sce$sample_name %in% c('151673', '151674')]
 #'
 #' ## Obtain the plot list
 #' p_list <-
@@ -40,6 +40,9 @@
 #'         colors = libd_layer_colors
 #'     )
 #'
+#' ## Clean up
+#' rm(sce_sub)
+#'
 #' ## Visualize the spatial adjacent replicates for position = 0 micro meters
 #' ## for subject 3
 #' cowplot::plot_grid(plotlist = p_list, ncol = 2)